Figure 1.

Perceptual decision making in a stationary context. (a) Design of a typical visual discrimination task. Subjects report the net direction of random-dots motion (left or right) by making a saccadic eye movement. The percentage of coherently moving dots varies across trials. Neural recording is typically made from neurons whose response field (RF, gray shading) overlaps with one of the targets. (b) A bounded accumulation model accounts for the behavior. The model accumulates noisy sensory evidence to form a decision variable (DV) and commits to a choice when the DV reaches a bound. (c) Neural activity similar to the DV can be found in diverse brain regions involved in oculomotor control. For example, neurons in LIP increase their firing rates when the stimulus supports a saccade to the target in their RFs. T_in, target in neuron RF; T_out, target outside RF. Right panel, adapted with permission from Reference 8; copyright 2002 Society for Neuroscience. (d) Circuit models. In bistable attractor dynamics models (left; 19, 20), two pools of neurons, each supporting one of the choices, compete until one pool dominates. In probabilistic population codes, networks that integrate neural activity can perform optimal evidence accumulation (right; adapted with permission from Reference 21; copyright 2008 Elsevier). dlPFC, dorsolateral prefrontal cortex; FEF, frontal eye field; LIP, lateral intraparietal area; MT, middle temporal area.